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Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Nori, H., Lee, Y.T., Zhang, S., Carignan, D., Edgar, R., Fusi, N., King, N., Larson, J., Li, Y., Liu, W., Luo, R., McKinney, S.M., Ness, R.O., Poon, H., Qin, T., Usuyama, N., White, C. and Horvitz, E. (2023). Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine. arXiv:2311.16452 [cs]. [online] Available at: https://arxiv.org/abs/2311.16452

General Annotation #

“Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine” by Harsha Nori et al. explores the capabilities of generalist foundation models like GPT-4 in specialized domains, particularly in medicine, without intensive domain-specific training. Contrasting with previous approaches that heavily relied on specialized training (e.g., BioGPT, Med-PaLM), this paper demonstrates that innovative prompt engineering can significantly enhance GPT-4’s performance on medical challenge benchmarks. The study introduces Medprompt, a novel prompting strategy that combines dynamic few-shot learning, self-generated chain of thought, and choice shuffling ensemble, resulting in state-of-the-art performance on multiple medical question-answering datasets.

Methodologies Used #

  • Dynamic Few-shot Learning: Dynamically selects few-shot examples based on semantic similarity to the test question, using an embedding model for question embeddings and k-NN clustering for example selection.
  • Self-Generated Chain of Thought (CoT): Uses GPT-4 to generate detailed explanations (CoT) for training examples, then selects examples where the generated CoT leads to the correct answer, filtering out potentially unreliable reasoning chains.
  • Choice Shuffling Ensemble: To counteract position bias and enhance answer consistency, the method shuffles answer choices and employs self-consistency techniques, selecting the most common answer across multiple reasoning paths.

Key Contributions #

  • Demonstrated that GPT-4, with the help of Medprompt, outperforms specialized models on medical question-answering benchmarks, achieving top results without domain-specific training or human-crafted prompts.
  • Introduced novel prompting strategies that leverage the model’s capabilities for dynamic example selection, self-generated reasoning, and robust ensemble decision-making.
  • Showed the generalizability of Medprompt beyond medicine to other domains, suggesting broad applicability in leveraging generalist models for specialized tasks.

Main Arguments #

  • Challenges the assumption that specialized domain knowledge or intensive model training is necessary for high performance in domain-specific tasks, showing that strategic prompt engineering can unlock deep specialist capabilities in generalist models.
  • Argues for the potential of generalist models like GPT-4, when combined with innovative prompting strategies, to significantly advance performance on specialized benchmarks, reducing the need for domain-specific model training.

Gaps #

  • The paper focuses on medical question-answering tasks, leaving the exploration of the methodology’s effectiveness across a wider range of specialized tasks for future research.
  • While it demonstrates generalization to other domains, the scope of tested domains remains limited, indicating the need for further studies to explore the full range of applicability.

Relevance to Prompt Engineering & Architecture #

This study underscores the transformative potential of prompt engineering in maximizing the utility of generalist foundation models for specialized tasks. By showcasing the effectiveness of Medprompt, it opens new avenues for research and application in prompt engineering and model architecture, highlighting the feasibility of leveraging large, generalist models across diverse domains without the necessity for extensive retraining. The findings suggest a paradigm shift towards more flexible and efficient use of foundation models, potentially influencing the development of future models and prompting strategies.

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Updated on March 31, 2024